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Danielsson, J., Tsog, N. & Kunnappilly, A. (2019). A systematic mapping study on real-Time cloud services. In: Proceedings - 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018: . Paper presented at 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018; Zurich; Switzerland; 17 December 2018 through 20 December 2018 (pp. 245-251). Institute of Electrical and Electronics Engineers Inc., Article ID 8605787.
Open this publication in new window or tab >>A systematic mapping study on real-Time cloud services
2019 (English)In: Proceedings - 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 245-251, article id 8605787Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is relatively a new technique to host and use the services and applications from the internet. Although it offers a multitude of advantages like scalability, low operating cost, accessibility and maintainability, etc., they are often not utilized to the fullest due to the lack of timeliness property associated with the cloud. Cloud services are mainly designed to maximize throughput and utilization of resources and hence incorporating predictable execution time properties in to the cloud is arduous. Nevertheless, cloud still remains a highly attractive platform for hosting real-Time applications and services owing to features like elasticity, multi-Tenancy, ability to survive hardware failures, virtualization support and abstraction layer support which provides flexibility and portability. In order for real-Time safety-critical applications to exploit the potential of cloud computing, it is essential to ensure the predictable real-Time behavior of cloud services. In this paper, we perform a systematic mapping study on real-Time cloud services to identify the current research directions and potential research gaps. Our study focuses on analyzing the current architectures and software techniques that are available at present to incorporate real-Time property of the cloud services. We also aim at investigating the current challenges involved in realizing a predictable real-Time behavior of cloud services. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-42812 (URN)10.1109/UCC-Companion.2018.00063 (DOI)000458720100047 ()2-s2.0-85061834817 (Scopus ID)9781728103594 (ISBN)
Conference
11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018; Zurich; Switzerland; 17 December 2018 through 20 December 2018
Available from: 2019-02-28 Created: 2019-02-28 Last updated: 2019-02-28Bibliographically approved
Tsog, N., Sjödin, M. & Bruhn, F. (2019). A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated Real-Time Systems. In: : . Paper presented at The 32nd International Symposium on Space Technology and Science, Fukui, Japan.
Open this publication in new window or tab >>A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated Real-Time Systems
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

On-board data processing is one of the prior on-orbit activities that it improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters with higher energy consumption compared to traditional space systems. Because traditional space systems employ simple processing units such as micro-controllers or a single-core processor as the systems require no heavy data processing on orbit. Moreover, solving the radiation hardness problem is crucial in space and adopting a new processing unit is challenging.

In this paper, we consider a GPU accelerated real-time system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of power consumption and computing potential using this heterogeneous computing method in our GPU accelerated real-time system.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.

Keywords
Trade-off, Computing power, Energy consumption, on-board data processing, GPU acceleration, Real-time systems
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45938 (URN)
Conference
The 32nd International Symposium on Space Technology and Science, Fukui, Japan
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-22Bibliographically approved
Tsog, N. (2019). Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems. (Licentiate dissertation). Västerås: Mälardalen University
Open this publication in new window or tab >>Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates the efficacy of heterogeneous computing architectures in real-time systems.The goals of the thesis are twofold. First, to investigate various characteristics of the Heterogeneous System Architectures (HSA) compliant reference platforms focusing on computing performance and power consumption. The investigation is focused on the new technologies that could boost on-board data processing systems in satellites and spacecraft. Second, to enhance the usage of the heterogeneous processing units by introducing a technique for static allocation of parallel segments of tasks.

The investigation and experimental evaluation show that our method of GPU allocation for the parallel segments of tasks is more energy efficient compared to any other studied allocation. The investigation is conducted under different types of environments, such as process-level isolated environment, different software stacks, including kernels, and various task set scenarios. The evaluation results indicate that a balanced use of heterogeneous processing units (CPU and GPU) could improve schedulability of task sets up to 90% with the proposed allocation technique.

Abstract [sv]

Denna avhandling undersöker effektiviteten hos heterogena datorarkitekturer i realtidssystem. Målet med avhandlingen är tvåfaldigt. Till att börja med, att undersöka olika egenskaper hos plattformar baserade på Heterogeneous System Architecture, med fokus på datorprestanda och strömförbrukning. Undersökningen är inriktad på tekniker som kan öka datorbehandlingssystemen ombord i satelliter och rymdskepp. För det andra förbättra användningen av heterogena arkitekturer genom att införa en teknik för statisk allokering av parallella programsegment.

Undersökningen och den experimentella utvärderingen visar att vår metod för effektiv användning av GPU-allokering för parallella programsegment är den mest energieffektiva jämfört med någon annan studerad allokering. Undersökningarna har genomförts i olika typer av miljöer, såsom processisolerad miljö, olika mjukvarustackar, inklusive kernel, och olika uppsättningsscenarier. Utvärderingsresultaten indikerar dessutom att en balanserad användning av heterogena beräkningsenheter (CPU och GPU) kan förbättra schemaläggningen för vissa program upp till 90% jämfört med de tidigare föreslagna allokeringsteknikerna.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 286
Keywords
on-board data processing, CPU-GPU, heterogeneous architectures, real-time systems
National Category
Engineering and Technology Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45940 (URN)978-91-7485-450-3 (ISBN)
Presentation
2019-12-18, Kappa, Mälardalens högskola, Västerås, 09:15 (English)
Opponent
Supervisors
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-19Bibliographically approved
Tsog, N., Becker, M., Bruhn, F., Behnam, M. & Nolin, M. (2019). Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems. In: : . Paper presented at IEEE 45th Annual Conference of the Industrial Electronics Society, IECON2019.
Open this publication in new window or tab >>Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous driving is one of the main challenges of modern cars. Computer visions and intelligent on-board decision making are crucial in autonomous driving and require heterogeneous processors with high computing capability under low power consumption constraints. The progress of parallel computing using heterogeneous processing units is further supported by software frameworks like OpenCL, OpenMP, CUDA, and C++AMP. These frameworks allow the allocation of parallel computation on different compute resources. This, however, creates a difficulty in allocating the right computation segments to the right processing units in such a way that the complete system meets all its timing requirements. In this paper, we consider pre-runtime static allocations of parallel tasks to perform their execution either sequentially on CPU or in parallel using a GPU. This allows for improving any unbalanced use of GPU accelerators in a heterogeneous environment. By performing several heuristic algorithms, we show that the overuse of accelerators results in a bottle-neck of the entire system execution. The experimental results show that our allocation schemes that target a balanced use of GPU improve the system schedulability up to 90%.

Keywords
Parallel task, Parallel segment, Alternative execution, CPU-GPU, Heterogeneous processors, Real-time systems
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45934 (URN)
Conference
IEEE 45th Annual Conference of the Industrial Electronics Society, IECON2019
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-12-13Bibliographically approved
Tsog, N., Nolin, M. & Bruhn, F. (2019). Using Docker in Process Level Isolation for Heterogeneous Computing on GPU Accelerated On-Board Data Processing Systems. In: : . Paper presented at 12th IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany.
Open this publication in new window or tab >>Using Docker in Process Level Isolation for Heterogeneous Computing on GPU Accelerated On-Board Data Processing Systems
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The technological advancements make the intelligent on-board data processing possible on a small scale of satellites and deep-space exploration spacecraft such as CubeSats. However, the operation of satellites may fall into critical conditions when the on-board data processing interferes strongly to the basic operation functionalities of satellites. In order to avoid these issues, there exist techniques such as isolation, partitioning, and virtualization. In this paper, we present an experimental study of isolation of on-board payload data processing from the basic operations of satellites using Docker. Docker is a leading technology in process level isolation as well as continuous integration and continuous deployment (CI/CD) method. This study continues with the prior study on heterogeneous computing method, which improves the schedulability of the entire system up to 90%. Based on this heterogeneous computing method, the comparison study has been conducted between the non-isolated and isolated environments.

Keywords
Process level isolation, Docker, On-board data processing, Heterogeneous computing, cgroups, Linux
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45939 (URN)
Conference
12th IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-12-13Bibliographically approved
Tsog, N., Sjödin, M. & Bruhn, F. (2019). Using Heterogeneous Computing on GPU Accelerated Systems to Advance On-Board Data Processing. In: European Workshop on On-Board Data Processing 2019 OBDP2019: . Paper presented at European Workshop on On-Board Data Processing 2019 OBDP2019, 25 Feb 2019, Amsterdam, Netherlands.
Open this publication in new window or tab >>Using Heterogeneous Computing on GPU Accelerated Systems to Advance On-Board Data Processing
2019 (English)In: European Workshop on On-Board Data Processing 2019 OBDP2019, 2019Conference paper, Published paper (Refereed)
Keywords
Heterogeneous Computing, GPU accelerated On-Board Data Processing, Advanced On-Board Data Processing
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45490 (URN)
Conference
European Workshop on On-Board Data Processing 2019 OBDP2019, 25 Feb 2019, Amsterdam, Netherlands
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-10-29Bibliographically approved
Tsog, N., Sjödin, M. & Bruhn, F. (2018). Advancing On-Board Big Data Processing Using Heterogeneous System Architecture. In: ESA/CNES 4S Symposium 4S 2018: . Paper presented at ESA/CNES 4S Symposium 4S 2018, 28 May 2018, Sorrento, Italy.
Open this publication in new window or tab >>Advancing On-Board Big Data Processing Using Heterogeneous System Architecture
2018 (English)In: ESA/CNES 4S Symposium 4S 2018, 2018Conference paper, Poster (with or without abstract) (Refereed)
Keywords
Heterogeneous System Architecture (HSA)Onboard ProcessingBig DataCPU-GPUCaffe (Convolutional Architecture for Fast Feature Embedding)ROCmSmall SatelliteCubeSatImagenet
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39269 (URN)
Conference
ESA/CNES 4S Symposium 4S 2018, 28 May 2018, Sorrento, Italy
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2018-05-23Bibliographically approved
Tsog, N., Behnam, M., Nolin, M. & Bruhn, F. (2018). Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture. In: IEEE Aerospace Conference 2018 IEEEAC2018: . Paper presented at IEEE Aerospace Conference 2018 IEEEAC2018, 03 Mar 2018, Big Sky, United States (pp. 1-8).
Open this publication in new window or tab >>Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture
2018 (English)In: IEEE Aerospace Conference 2018 IEEEAC2018, 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library 'MIOpen' and an open standard 'OpenVX'. OpenVX is a cross-platform computer vision library.

Series
IEEE Aerospace Conference Proceedings, ISSN 1095-323X
Keywords
Heterogeneous System Architecture (HSA)Intelligent Data ProcessingMIOpenOpenVXCubeSatCPU-GPUEnergy consumption
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38628 (URN)10.1109/AERO.2018.8396536 (DOI)2-s2.0-85049840022 (Scopus ID)
Conference
IEEE Aerospace Conference 2018 IEEEAC2018, 03 Mar 2018, Big Sky, United States
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2019-11-11Bibliographically approved
Tsog, N., Behnam, M., Sjödin, M. & Bruhn, F. (2018). Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture. In: 2018 IEEE AEROSPACE CONFERENCE: . Paper presented at IEEE Aerospace Conference, MAR 03-10, 2018, Big Sky, MT. IEEE
Open this publication in new window or tab >>Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture
2018 (English)In: 2018 IEEE AEROSPACE CONFERENCE, IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library "MIOpen" and an open standard "OpenVX". OpenVX is a cross-platform computer vision library.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Aerospace Conference Proceedings, ISSN 1095-323X
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-46357 (URN)000474397401066 ()978-1-5386-2014-4 (ISBN)
Conference
IEEE Aerospace Conference, MAR 03-10, 2018, Big Sky, MT
Available from: 2019-12-13 Created: 2019-12-13 Last updated: 2019-12-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8096-3891

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